Method of outputting estimated qoes on a terminal on an application basis

ABSTRACT

Implementations and techniques for outputting information about estimated QoEs on a terminal on which plural applications can be executed are generally disclosed. The estimated QoEs may be obtained by performing QoE estimation on an application basis.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S.application Ser. No. 13/266,579, filed Oct. 27, 2011; which is a U.S.National Stage application of PCT/JP2011/004805, filed Aug. 29, 2011,the entire disclosures of which are incorporated herein by reference.

BACKGROUND

Unless otherwise indicated herein, the approaches described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

For a user who wants to uses a particular application on a terminal, itis useful to know how much QoE can be obtained if the application isactually performed on a terminal.

Therefore, it is desired to provide useful ways of outputtinginformation about estimated QoEs on a terminal.

SUMMARY

According to an embodiment, a method of outputting information aboutestimated QoEs on a terminal is provided. The method may includeperforming QoE estimation on an application basis; and outputting, basedon the QoE estimation, information about estimated QoEs on the terminal.

BRIEF DESCRIPTION OF DRAWINGS

Subject matter is particularly pointed out and distinctly claimed in theconcluding portion of the specification. The foregoing and otherfeatures of the present disclosure will become more fully apparent fromthe following description and appended claims, taken in conjunction withthe accompanying drawings. Understanding that these drawings depict onlyseveral embodiments in accordance with the disclosure and are,therefore, not to be considered limiting of its scope, the disclosurewill be described with additional specificity and detail through use ofthe accompanying drawings.

FIG. 1 shows a conceptual diagram of cloud computing.

FIG. 2 is a flowchart for showing a process executed by the terminal 10.

FIG. 3 is a diagram for showing an example of information displayed on adisplay 12 of the terminal 10.

FIG. 4 is a diagram for showing an example of the QoE estimation for theapplication “Web Album”.

FIG. 5 is a diagram for showing an example of the QoE estimation for theapplication “Video distribution”.

FIG. 6 is a diagram for showing an example of the QoE estimation for theapplication “IP phone”.

FIG. 7 is a flowchart for showing an example of a way of setting thecriterion used in estimating the QoE.

FIG. 8 is a functional block diagram for showing an example of theconfiguration of the terminal 10.

FIG. 9 is a flowchart for showing an example of the operations of thefuture action predicting part 102 and the QoE estimation triggering part104.

FIG. 10 is a flowchart for showing another example of the operations ofthe future action predicting part 102 and the QoE estimation triggeringpart 104.

DETAILED DESCRIPTION

The following description sets forth various examples along withspecific details to provide a thorough understanding of claimed subjectmatter. It will be understood by those skilled in the art, however, thatclaimed subject matter may be practiced without some or more of thespecific details disclosed herein. Further, in some circumstances,well-known methods, procedures, systems, components and/or circuits havenot been described in detail in order to avoid unnecessarily obscuringclaimed subject matter.

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe Figures, can be arranged, substituted, combined, and designed in awide variety of different configurations, all of which are explicitlycontemplated and make part of this disclosure.

FIG. 1 shows a conceptual diagram of cloud computing. As shown in FIG.1, a user of a terminal 10 can enjoy various applications, such asE-mail, Calendar, etc., in a cloud 20 or via a cloud 20.

The terminal 10 may be of any type. The terminal 10 may be a smartphone,a tablet, a laptop computer, a desktop computer or the like.

The cloud 20 may be an inclusive element which includes elements ofcommunication networks to be accessed by the terminal 10. In otherwords, the cloud 20 may include the elements which enable applicationsto be executed on the terminal 10. The cloud 20 may include the Internetand various service providers such as [Google], [Yahoo], etc.

The terminal 10 may be configured to perform QoE estimation on anapplication basis. Specifically, the terminal 10 may be configured toestimate a QoE (Quality of Experience) for the respective applications,which can be executed on the terminal 10, on an application basis.

Here, the QoE (Quality of Experience) of an application may depend onfactors such as the status of the wireless access network, the status ofthe core network, and the status inside the cloud 20. The QoE mayindicate quality of communication required by a user. Thus, the QoE maydiffer from application to application. Further, the QoE may differ fromuser to user.

A way of estimating the QoE for the application may be various. Any waymay be used as long as it can estimate the QoE on an application basis.Some examples of the way of estimating the QoE for the application aredescribed hereinafter.

The terminal 10 may be configured to output information about estimatedQoEs of the respective applications on a display of the terminal. Theinformation may be output in various manners. For example, the estimatedQoEs of the respective applications may be directly displayed on thedisplay of the terminal. Some examples of the way of outputtinginformation about estimated QoEs of the respective applications aredescribed hereinafter.

FIG. 2 is a flowchart for showing a process executed by the terminal 10.

In step 200, the QoE estimation may be performed on an applicationbasis. For example, if there are four applications, the QoE estimationmay be performed for each of the four applications separately. Timing ofQoE estimation for each of the four applications may be arbitrary. Forexample, the QoE estimation for each of the four applications may beperformed successively as batch processing or performed at intervalsover time.

The QoE estimation may be performed using a criterion. The criterion maybe different from application to application. Thus, the criterion may beset on an application basis. The criterion may be set in advance as adefault criterion. The criterion may be set based on user data of theterminal. The user data may include information which indicates needs orpreferences of the user of the terminal 10. Such information may bederived statistically from the data obtained during actual operations ofthe terminal 10 by the user, or may be input directly by the user. Forexample, the default criterion may be corrected according to the inputfrom the user.

In step 202, the information about estimated QoEs of the respectiveapplications may be output on the display of the terminal. Theinformation about estimated QoE of the application may be output(updated) when the QoE estimation for the application has completed.Thus, the information about estimated QoEs of the respectiveapplications may be output (updated) at different timings. Theinformation about estimated QoE of the application may be output(updated) before the application is actually executed on the terminal10.

FIG. 3 is a diagram for showing an example of information displayed on adisplay 12 of the terminal 10. In FIG. 3, as an example, sevenapplications (E-mail, Web Album, Map, Video distribution, IP phone,Calendar and Street View) can be executed on the terminal 10.

As shown in FIG. 3, the terminal 10 may be configured to display icons14 a-14 g. The number of icons may depend on the number of theapplications. The icons 14 a-14 g may be provided for initiating thecorresponding applications. In other words, the icons 14 a-14 g may belaunchers for the corresponding applications. The respectiveapplications may be initiated when the corresponding icons 14 a-14 g areactivated. The activation of the icons 14 a-14 g may be implemented by aclick operation, a touch operation or the like. The icons 14 a-14 g mayinclude letters or illustrations that indicate the correspondingapplications, as shown in FIG. 3.

The terminal 10 may be configured to change colors of icons displayed onthe terminal 10 according to the QoE estimation. For example, “Blue” mayindicate QoE of “Very good”, “Green” may indicate QoE of “Good”,“Yellow” may indicate QoE of “Fair”, “Red” may indicate QoE of “Bad”,and “Gray” may indicate a QoE of “Unusable”. For example, if theestimated QoE for the application “E-mail” associated with the icon 14 ais “Good”, the color of the icon 14 a may be changed to yellow. Withthis arrangement, the estimated QoEs can be easily distinguished bycolors.

It is noted that the icons 14 a-14 g may be replaced with links in thecase of Web sites or e-mails. The links are often provided on the Websites or the e-mails. In this case, the links function as launchers forexecuting the applications. Thus, in this case, the status links, suchas colors of the links, may be changed according to the estimated QoEsof the corresponding applications.

FIG. 4 is a diagram for showing an example of the QoE estimation for theapplication “Web Album”. The application “Web Album” may be anapplication for browsing (viewing) pictures (photos) in the cloud 20.The application “Web Album” may be used for sharing particular pictureswith particular parties such as friends and family.

The QoE estimation for the application “Web Album” may be performed byfollowing steps 1-3.

In step 1, the terminal 10 may request browsing of the picture in thecloud 20. Specifically, the terminal 10 may issue a request for testdata of the picture to the cloud 20. The test data may be a part of thenormal picture as schematically shown in FIG. 4. In other words, thetest data may be smaller in a data size than the normal picture. Thetest data may be prepared specially for the QoE estimation.

In step 2, the cloud 20 may transfer the requested picture to theterminal 10. In other words, the terminal 10 may receive the test dataof the picture from the cloud 20.

In step 3, the terminal 10 may measure the transfer time between therequest and the reception of the test data of the picture at theterminal 10.

In step 4, the terminal 10 may estimate the QoE for the application “WebAlbum” based on the measured transfer time. For example, therelationship between the measured transfer time and a criterion may bebased on in estimating the QoE. If the measured transfer time issufficiently shorter than a transfer time according to the criterion,the QoE may be estimated as “Very good”. If the measured transfer timeis not sufficiently but shorter than a transfer time according to thecriterion, the QoE may be estimated as “Good”. Similarly, if themeasured transfer time is considerably longer than a transfer timeaccording to the criterion, the QoE may be estimated as “Bad”. If themeasured transfer time is not considerably but longer than a transfertime according to the criterion, the QoE may be estimated as “Fair”. Ifthe test data of the picture cannot be received at the terminal 10, theQoE may be estimated as “Unusable”. When the estimated QoE is high suchas “Very good” or “Good”, the user may be recommended to browsehigh-quality pictures. On the other hand, when estimated QoE is low suchas “Bad”, the user may be recommended to browse low-quality pictures.

FIG. 5 is a diagram for showing an example of the QoE estimation for theapplication “Video distribution”. The application “Video distribution”may be an application for browsing (viewing) video in the cloud 20. Theapplication “Video distribution” may be such as [YouTube].

The QoE estimation for the application “Video distribution” may beperformed by following steps 1-3.

In step 1, the terminal 10 may request browsing of the video in thecloud 20. Specifically, the terminal 10 may issue a request for testdata of the video to the cloud 20. The test data may be a part of thenormal video as schematically shown in FIG. 5. In other words, the testdata may be smaller in a data size than the normal video. The test datamay be prepared specially for the QoE estimation.

In step 2, the cloud 20 may transfer the requested video to the terminal10. In other words, the terminal 10 may receive the test data of thevideo from the cloud 20.

In step 3, the terminal 10 may measure the transfer time between therequest and the reception of the test data of the video at the terminal10. The terminal 10 may also measure a packet loss rate during thetransfer.

In step 4, the terminal 10 may estimate the QoE for the application“Video distribution” based on the measured transfer time and packet lossrate. For example, the relationship between the measured transfer timeand packet loss rate and a criterion may be the basis in estimating theQoE. If the measured transfer time and packet loss rate are sufficientlysmaller than a transfer time and a packet loss rate according to thecriterion, the QoE may be estimated as “Very good”. If the measuredtransfer time and packet loss rate are not sufficiently but smaller thana transfer time and a packet loss rate according to the criterion, theQoE may be estimated as “Good”. Similarly, if the measured transfer timeand packet loss rate are considerably greater than a transfer time and apacket loss rate according to the criterion, the QoE may be estimated as“Bad”. If the measured transfer time and packet loss rate are notconsiderably but greater than a transfer time and a packet loss rateaccording to the criterion, the QoE may be estimated as “Fair”. If thetest data of the video cannot be received at the terminal 10, the QoEmay be estimated as “Unusable”. When the estimated QoE is high such as“Very good” or “Good”, the user may be recommended to browsehigh-quality video. On the other hand, when estimated QoE is low such as“Bad”, the user may be recommended to browse low-quality video.

FIG. 6 is a diagram for showing an example of the QoE estimation for theapplication “IP phone”. The application “IP phone” may be an applicationfor telephone service using IP (Internet Protocol).

The QoE estimation for the application “IP phone” may be performed byfollowing steps 1-3.

In step 1, the terminal 10 may request transmission of the voice in thecloud 20. Specifically, the terminal 10 may issue a request for testdata of the voice to the cloud 20. The test data may be a part of thenormal voice data as schematically shown in FIG. 6. In other words, thetest data may be smaller in a data size than the normal voice data. Thetest data may be prepared specially for the QoE estimation.

In step 2, the cloud 20 may transfer the requested voice data to theterminal 10. In other words, the terminal 10 may receive the test dataof the voice from the cloud 20.

In step 3, the terminal 10 may measure the transfer time between therequest and the reception of the test data of the voice at the terminal10. The terminal 10 may also measure a packet loss rate during thetransfer.

In step 4, the terminal 10 may estimate the QoE for the application “IPphone” based on the measured transfer time and packet loss rate. Forexample, the relationship between the measured transfer time and packetloss rate and a criterion may be the basis in estimating the QoE. If themeasured transfer time and packet loss rate are sufficiently smallerthan a transfer time and a packet loss rate according to the criterion,the QoE may be estimated as “Very good”. If the measured transfer timeand packet loss rate are not sufficiently but smaller than a transfertime and a packet loss rate according to the criterion, the QoE may beestimated as “Good”. Similarly, if the measured transfer time and packetloss rate are considerably greater than a transfer time and a packetloss rate according to the criterion, the QoE may be estimated as “Bad”.If the measured transfer time and packet loss rate are not considerablybut greater than a transfer time and a packet loss rate according to thecriterion, the QoE may be estimated as “Fair”. If the test data of thevoice cannot be received at the terminal 10, the QoE may be estimated as“Unusable”.

It is noted that in the examples shown in FIGS. 4-6, any quality factorother than the transfer time and the packet loss rate may be used. Forexample, quality factor may be any factors related to QoS (Quality ofService), such as S/N. Further, fluctuations in the transfer time andthe packet loss rate may be used as quality factors. Similarly, astandard deviation or a variance of the transfer time and the packetloss rate may be used as quality factors.

FIG. 7 is a flowchart for showing an example of a way of setting thecriterion used in estimating the QoE. The process routine may beperformed by the terminal 10. The process routine may be initiated whenthe change in the criterion is requested. The user may be invited todetermine whether to change the criterion when the application isterminated. If the user feels that the output information about the QoEwith respect to the application now terminated does not correspond tothe user's experience, the user may change the criterion. It is notedthat any user data may be input to the terminal 10 via any userinterfaces.

In step 700, it may be determined whether the change in the criterion isrequested by numerical values. If the change in the criterion isrequested by numerical values, the process routine goes to step 702.Otherwise, the process routine goes to step 704.

In step 702, the numerical values from the user may be input. In otherwords, the terminal 10 may receive user input which indicates thenumerical values.

In step 704, the QoE from the user may be input. In other words, theterminal 10 may receive user input which indicates the QoE the userassesses. The input QoE may be based on the actual experience of theuser. In this way, the data of QoE from the user may be accumulatedbased on the execution of the applications. The data of QoE from theuser may be stored in a memory (database) on an application basis. Inother words, the data of QoE may be stored such that it is associatedwith the corresponding applications.

In step 706, statistical estimation may be performed based on theaccumulated data of QoE from the user. The statistical estimation may beperformed to estimate needs or preferences of the user of the terminal10. For example, the statistical estimation may be performed todetermine which quality factor affects the QoE the most.

In step 708, the criterion may be updated (adjusted) based on thenumerical values. For example, the criterion may be made stricter ormore lenient according to the user input. The criterion may be updated(adjusted) based on the statistical estimation. For example, if thestatistical estimation indicates that the user places prime importanceon speed in the application “Video distribution”, the criterion relatedto the transfer time may be made stricter.

FIG. 8 is a functional block diagram for showing an example of theconfiguration of the terminal 10. It is noted that the example shown inFIG. 8 may be implemented independently from other examples describedabove.

The terminal 10 may include a future action predicting part 102 and aQoE estimation triggering part 104.

The future action predicting part 102 may predict the user's futureaction. In other words, the future action predicting part 102 maypredict which application(s) the user of the terminal 10 uses in thefuture. For example, the future action predicting part 102 may predictwhich application(s) the user of the terminal 10 will use within apredetermined time. The predetermined time may be set such that it islonger than the time necessary to perforin the QoE estimation.

The QoE estimation triggering part 104 may determine the timing ofperforming the QoE estimation. The QoE estimation triggering part 104may trigger the QoE estimation at a predetermined interval. Thepredetermined interval may be measured by a timer 106. Further, the QoEestimation triggering part 104 may trigger the QoE estimation inresponse to a user instruction. The user instruction may be input viathe user interface 108.

Further, the QoE estimation triggering part 104 may trigger the QoEestimation based on the prediction of the future action predicting part102. In other words, the QoE estimation triggering part 104 maydetermine for which application the QoE estimation is to be performednext and determine the timing of starting the QoE estimation. The timingof triggering (starting) the QoE estimation for the application may besuch that the information about the estimated QoE based on the QoEestimation is output before the application is actually executed.

FIG. 9 is a flowchart for showing an example of the operations of thefuture action predicting part 102 and the QoE estimation triggering part104.

In step 902, the future action predicting part 102 may predict an orderin which applications are to be executed on the terminal 10. The futureaction predicting part 102 may predict the order based on thestatistical information 130. The statistical information 130 may beaccumulated based on the past user's actions.

There may be tendencies of the order in which applications are to beexecuted on the terminal 10. As examples, the following action patternsmay be stored as the statistical information 130.

Action pattern 1: Power on (or Activation)-E-mail(receiving)-Calendar-IP phone-Map-Street View-IP phone-Email(receiving);

Action pattern 2: Power on (or Activation)-E-mail(receiving)-Browser-E-mail (sending)-Web Album;

Action pattern 3: Power on (or Activation)-E-mail (receiving)-WebAlbum-IP phone-Browser-Calendar; and

Action pattern 4: Power on (or Activation)-E-mail (receiving)-IPphone-Browser-Calendar.

Such action patterns may be stored such that they are associated withsituations or circumstances such as time (morning, afternoon, night,weekdays, weekends, holiday, etc.), locations (outdoor, indoor, on atrain, etc.). In this case, the future action predicting part 102 maypredict the action pattern based on the current situation orcircumstance. Further, the future action predicting part 102 may simplypredict the action pattern most frequently detected.

In step 904, the QoE estimation triggering part 104 may determine theorder of the QoE estimation for the respective applications according tothe order predicted in step 902. In other words, the QoE estimationtriggering part 104 may trigger the QoE estimation for the respectiveapplications according to the order predicted in step 902. For example,if the Action pattern 1 is predicted, the QoE estimation triggering part104 may trigger the QoE estimation for the application “E-mail” first,the QoE estimation for the application “Calendar” second, and so on.

The results of the QoE estimation may be output on the terminal in themanner described above.

FIG. 10 is a flowchart for showing another example of the operations ofthe future action predicting part 102 and the QoE estimation triggeringpart 104.

In step 1002, the future action predicting part 102 may predictprobabilities of the respective applications being executed on theterminal 10.

The future action predicting part 102 may predict the probabilitiesbased on the statistical information 130. The statistical information130 may be accumulated based on the past user's actions.

For example, the future action predicting part 102 may simply predictthe probabilities based on the frequencies of usage of the respectiveapplications. In this case, the future action predicting part 102 maysimply predict the highest probability for the most frequently usedapplication.

The future action predicting part 102 may predict the probabilitiesbased on information 140 about the currently used application and thestatistical information 130. For example, in the case of the Actionpatterns 1-4 being stored as the statistical information 130, afterusing the application “E-mail” for receiving e-mails, four applications,namely, Calendar, Browser, Web Album and IP phone may be used next.Thus, the future action predicting part 102 may increase probabilitieswith respect to these four applications. In this case, the future actionpredicting part 102 may compare frequencies of usage between these fourapplications to determine the application with the highest probabilityamong them.

The future action predicting part 102 may predict the probabilitiesbased on information 140 about the E-mail contents. The E-mail contentsmay be contents of the E-mail currently opened (displayed) on theterminal 10. In other words, the E-mail contents may be contents of theE-mail the user of the terminal 10 is currently browsing. The E-mailcontents may include date information, schedule inquiry, or the like.The E-mail contents may include a Web link. The E-mail contents mayinclude a picture file attached therein and a video file attachedtherein. The E-mail contents may include a message asking for a callwith or without the IP phone number. These contents may be analyzed byan analyzer of the terminal 10. In this case, if the E-mail contentsinclude date information, schedule inquiry, or the like, the futureaction predicting part 102 may predict the increased probability for theapplication “Calendar”, for example. Similarly, if the E-mail contentsinclude the Web link, the future action predicting part 102 may predictthe increased probability for the application “Browser”, for example. Ifthe E-mail contents include the picture file, the future actionpredicting part 102 may predict the increased probability for theapplication “Web Album”, for example. If the E-mail contents include thevideo file, the future action predicting part 102 may predict theincreased probability for the application “Video distribution”, forexample. If the E-mail contents include the message asking for a call,the future action predicting part 102 may predict the increasedprobability for the application “IP phone”, for example.

The future action predicting part 102 may predict the probabilitiesbased on information 140 about the Web contents, as is the case with theE-mail contents.

The future action predicting part 102 may predict the probabilitiesbased on information 140 about date and time. In the case of weekends(i.e., Saturday and Sunday), the future action predicting part 102 maypredict the increased probabilities for the applications “Music”,“Game”, “Navigation”, “Web Album” and “News (general, economy, inparticular)”, for example. In the case of weekdays, the future actionpredicting part 102 may predict the increased probability for theapplication “News”, for example. In the case of morning, the futureaction predicting part 102 may predict the increased probabilities forthe applications “Schedule”, “E-mail (reading)” and “News (weather,traffic information, in particular)”, for example. In the case ofafternoon or evening, the future action predicting part 102 may predictthe increased probability for the application “E-mail (writing)”, forexample. In the case of night, the future action predicting part 102 maypredict the increased probabilities for the applications “Music”,“Game”, “Navigation”, “Web Album” and “News (general, economy, inparticular)”, for example.

The future action predicting part 102 may predict the probabilitiesbased on information 120 from sensors installed in the terminal 10. Theinformation 120 from sensors may be such information that indicates anenvironment in which the terminal 10 is located. For example, sensorsmay include at least one of a GPS receiver, an accelerometer (or avibration sensor), an illuminance sensor, a microphone sensor(interface), a power supply connection sensor (interface), and anearphone connection sensor (interface).

For example, the future action predicting part 102 may determine whetherthe user of the terminal 10 is on the move or stationary based on theinformation from the GPS receiver. If the user is on the move; thefuture action predicting part 102 may predict the increasedprobabilities for the applications “E-mail” and “IPTV”, for example. Onthe other hand, if the user is on the move, the future action predictingpart 102 may predict the decreased probability for the application “IPphone”, for example. If the user is stationary, the future actionpredicting part 102 may predict the increased probabilities for theapplications “TV conference”, “Browser (Web)” and “IP phone”, forexample.

The information from the GPS receiver and the map information may becross-referenced to infer the location of the user (i.e., terminal 100).In other words, map matching techniques used in a field of the carnavigation may be used. If the user is on a train, the future actionpredicting part 102 may predict the increased probabilities for theapplications “IPTV”, “E-mail” and “Twitter”, for example. If the user iswalking along the road, the future action predicting part 102 maypredict the increased probabilities for the applications “IP phone”,“Radio”, “Map” and “Street View”, for example. If the user is drivingthe car, the future action predicting part 102 may predict the increasedprobability for the application “Navigation”, for example. If the useris in a library, the future action predicting part 102 may predict theincreased probabilities for the applications “E-mail” and “Map”, forexample. On the other hand, if the user is in a library, the futureaction predicting part 102 may predict the decreased probabilities forthe applications “IP phone” and “TV conference”, for example. If theuser is in a bank, the future action predicting part 102 may predict theincreased probabilities for the applications “News (stocks, foreignexchange, in particular)” “IP phone” and “Email”, for example. It isnoted that the accelerometer may be used as in the case of the GPSreceiver described above.

The future action predicting part 102 may determine whether the user ofthe terminal 10 is in an outdoor environment, an indoor environment, ora dark environment. If the user is in an outdoor environment, the futureaction predicting part 102 may predict the increased probabilities forthe applications “Navigation”, “Map” and “Street View”, for example. Ifthe user is in an indoor environment, the future action predicting part102 may predict the increased probabilities for the applications“Browser”, “Game”, “Email”, “Twitter” and “IPTV”, for example. If theuser is in a dark environment, the future action predicting part 102 maypredict the increased probability for the application “Music”, forexample.

The future action predicting part 102 may determine whether the user ison a train or driving a car based on the sound detected by themicrophone sensor. If the user is on a train, the future actionpredicting part 102 may predict the increased probabilities for theapplications “IPTV”, “E-mail” and “Twitter”, for example. If the user isdriving the car, the future action predicting part 102 may predict theincreased probability for the application “Navigation”, for example.

Further, if a microphone is connected to the terminal 10, the futureaction predicting part 102 may predict the increased probability for theapplication “IP phone”, for example. Similarly, if it is detected by theearphone connection sensor that an earphone is connected to the terminal10, the future action predicting part 102 may predict the increasedprobabilities for the applications “IPTV”, “IP phone”, “Radio” and“Music”, for example. Further, if it is detected by the power supplyconnection sensor that the battery of the terminal 10 is being charged,the future action predicting part 102 may determine that the user is inan indoor environment and thus may predict the increased probabilitiesfor the applications “Browser”, “Game”, “E-mail”, “Twitter” and “IPTV”,for example.

In step 1004, the QoE estimation triggering part 104 may determine theorder of the QoE estimation for the respective applications according tothe probabilities predicted in step 902. In other words, the QoEestimation triggering part 104 may trigger the QoE estimation for therespective applications according to the probabilities predicted in step902. For example, the QoE estimation triggering part 104 may trigger theQoE estimation for the respective applications in the decreasing orderof the probabilities. Further, the QoE estimation triggering part 104may stop triggering the QoE estimation for the application(s) whoseprobability is smaller than a predetermined threshold. In other words,the QoE estimation triggering part 104 may narrow the applications forwhich the QoE estimation is to be performed.

The results of the QoE estimation may be output on the terminal in themanner described above. For example, if the information 140 about theE-mail contents or the Web contents is used to determine theprobability, the results of the QoE estimation may be output on thee-mail or the web site in question. In this case, the information aboutestimated QoEs may be output on the terminal 10 such that a status (acolor, for example) of at least one of a Web link, an attached picturefile, an attached video file, a IP phone number, etc., is changedaccording to the estimated QoE for the corresponding application. Forexample, the color of the Web link, which is normally blue, may bechanged to “Red” if the estimated QoE for the application “Browser” is“Bad”.

It is noted that the order and the probability described above may bedetermined using any combination of the elements described above. Forexample, the future action predicting part 102 may predict probabilitiesof the respective applications being executed based on any combinationof the statistical information 130, the information 140 about thecurrently used application, the information 140 about the E-mailcontents, the information 140 about date and time, the information 120from sensors installed in the terminal 10.

It is also noted that the function of the future action predicting part102 may be implemented by the cloud 20. For example, the cloud 20 maystore statistical information about the histories of applicationoperations of the users in general, and predict the next application andoperation thereof based on information about both behaviors specific tothe user of the terminal 10 and specific to the users in general.

Some portions of the foregoing detailed description are presented interms of algorithms or symbolic representations of operations on databits or binary digital signals stored within a computing system memory,such as a computer memory. These algorithmic descriptions orrepresentations are examples of techniques used by those of ordinaryskill in the data processing arts to convey the substance of their workto others skilled in the art. An algorithm here, and generally, isconsidered to be a self-consistent sequence of operations or similarprocessing leading to a desired result. In this context, operations orprocessing involve physical manipulation of physical quantities.Typically, although not necessarily, such quantities may take the formof electrical or magnetic signals capable of being stored, transferred,combined, compared or otherwise manipulated. It has proven convenient attimes, principally for reasons of common usage, to refer to such signalsas bits, data, values, elements, symbols, characters, terms, numbers,numerals or the like. It should be understood, however, that all ofthese and similar terms are to be associated with appropriate physicalquantities and are merely convenient labels. Unless specifically statedotherwise, as apparent from the following discussion, it is appreciatedthat throughout this specification discussions utilizing terms such as“processing,” “computing,” “calculating,” “determining” or the likerefer to actions or processes of a computing device, that manipulates ortransforms data represented as physical electronic or magneticquantities within memories, registers, or other information storagedevices, transmission devices, or display devices of the computingdevice.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In some embodiments,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of fauns, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a flexible disk, a hard disk drive (HDD), a Compact Disc(CD), a Digital Versatile Disk (DVD), a digital tape, a computer memory,etc.; and a transmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunication link, a wireless communication link, etc.).

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely examples and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one and one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

While certain example techniques have been described and shown hereinusing various methods and systems, it should be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from the central concept described herein. Therefore, it isintended that claimed subject matter not be limited to the particularexamples disclosed, but that such claimed subject matter also mayinclude all implementations falling within the scope of the appendedclaims, and equivalents thereof.

What is claimed:
 1. A non-transitory computer readable medium storinginstructions that, when executed, cause one or more processors toperform operations for outputting information about estimated QoEs on aterminal on which plural applications can be executed, comprising:performing QoE estimation on an application basis; and outputting, basedon the QoE estimation, the information about the estimated QoEs on theterminal; wherein the QoE estimation is performed by issuing a requestfor test data to a cloud, receiving the test data from the cloud,measuring at least one quality factor based on the received test data,and estimating a QoE based on a relationship between the measuredquality factor and a criterion, the criterion being set based on theuser data of the terminal.
 2. The computer readable medium of claim 1,wherein the QoE estimation is performed using a criterion, the criterionbeing set based on user data of the terminal.
 3. The computer readablemedium of claim 1, wherein the applications include an application forbrowsing a picture in the cloud, the test data is picture data and thequality factor associated with the application for browsing the pictureincludes a transfer time between the request and the reception of thepicture data at the terminal.
 4. The computer readable medium of claim1, wherein the applications include an application for browsing video inthe cloud, the test data is video data and the quality factor associatedwith the application for browsing the video includes two factors, thefactors being a transfer time between the request and the reception ofthe video data at the terminal, and a packet loss rate during thetransfer of the video data.
 5. The computer readable medium of claim 1,wherein the applications include an application for an IP phone via thecloud, the test data is voice data and the quality factor associatedwith the application for the IP phone includes two factors, the factorsbeing a transfer time between the request and the reception of the voicedata at the terminal, and a packet loss rate during the transfer of thevoice data.
 6. The computer readable medium of claim 1, wherein theoutputting the information about the estimated QoEs on the terminalincludes changing colors of icons displayed on the terminal according tothe QoE estimation, the respective icons being provided for initiatingthe corresponding applications.
 7. The computer readable medium of claim1, further comprising predicting, based on statistical information,order in which applications are to be executed on the terminal, whereinthe order of the QoE estimation for the respective applications isdetermined according to the predicted order.
 8. The computer readablemedium of claim 7, wherein the statistical information is informationthat indicates histories of usage of the applications.
 9. The computerreadable medium of claim 1, further comprising predicting, based oninformation from sensors installed in the terminal, probabilities of therespective applications being executed on the terminal, wherein theapplication for which the QoE estimation is to be performed isdetermined according to the predicted probabilities.
 10. The computerreadable medium of claim 9, wherein the information from the sensors isinformation that indicates an environment in which the terminal islocated.
 11. The computer readable medium of claim 10, wherein thesensors include at least one of a GPS receiver, an accelerometer, amicrophone sensor, an illuminance sensor, a power supply connectionsensor, and an earphone connection sensor.
 12. The computer readablemedium of claim 1, further comprising predicting, based on informationcurrently displayed on the terminal, probabilities of the respectiveapplications being executed on the terminal, wherein the application forwhich the QoE estimation is to be performed is determined according tothe predicted probabilities.
 13. The computer readable medium of claim12, wherein the information currently displayed on the terminal includesat least one of a Web link, an attached picture file, an attached videofile, an IP phone number and an e-mail address, and the QoE estimationis performed for the application which is associated with theinformation currently displayed on the terminal.
 14. The computerreadable medium of claim 13, wherein the outputting the informationabout the estimated QoEs on the terminal includes changing a status ofat least one of the Web link, the attached picture file, the attachedvideo file, the IP phone number, and the e-mail address, according tothe QoE estimation for the corresponding application.
 15. The computerreadable medium of claim 9, wherein the information from the sensors isinformation that indicates whether the terminal is moving or isstationary.
 16. The computer readable medium of claim 10, wherein theenvironment in which the terminal is located includes at least one of anoutdoor environment, an indoor environment, or a dark environment.
 17. Asystem, for outputting information about estimated QoEs on a terminal onwhich plural applications can be executed, wherein the systemcomprising: a processor, and a QoE estimating triggering part configuredto: perform QoE estimation on an application basis by issuing a requestfor test data to a cloud, receiving the test data from the cloud,measuring at least one quality factor based on the received test data,and estimating a QoE based on a relationship between the measuredquality factor and a criterion, the criterion being set based on theuser data of the terminal; and output, based on the QoE estimation, theinformation about the estimated QoEs on the terminal.
 18. A systemcomprising: a cloud in communication with a terminal and configured topredict, based on statistical information, order or probabilities inwhich applications are to be executed on the terminal, and the terminalconfigured to: perform QoE estimation on an application basis; determineorder or the probabilities of the QoE estimation for the respectiveapplications according to the predicted order or probabilities; andoutput, based on the QoE estimation, information about estimated QoEs ona display of the terminal.
 19. A terminal on which plural applicationscan be executed, wherein the terminal is configured to: perform QoEestimation on an application basis; and output, based on the QoEestimation, information about estimated QoEs on a display of theterminal; wherein the QoE estimation is performed by issuing a requestfor test data to a cloud, receiving the test data from the cloud,measuring at least one quality factor based on the received test data,and estimating a QoE based on a relationship between the measuredquality factor and a criterion, the criterion being set based on theuser data of the terminal.
 20. The terminal of claim 19, wherein theterminal is further configured to: receive an application identifierfrom a cloud, wherein the application identifier is effective toidentify a next application of the plurality of applications on theterminal; identify, based on the application identifier, the nextapplication of the plurality of applications; and prioritize QoEestimation for the next application of the plurality of applications.21. The terminal of claim 19, wherein the terminal is further configuredto: predict, based on statistical information, order in whichapplications are to be executed on the terminal, wherein the order ofthe QoE estimation for the respective applications is determinedaccording to the predicted order, and wherein the statisticalinformation includes action patterns of a user.
 22. The terminal ofclaim 21, wherein the action patterns are associated with time andlocations at the time of the action occurring, and the terminal isfurther configured to predict action patterns of the user based oncurrent time and locations and predict the order in which applicationsare to be executed on the terminal based on the predicted actionpatterns of the user.
 23. The terminal of claim 19, wherein the terminalis further configured to: predict, based on information from sensorsinstalled in the terminal, probabilities of the respective applicationsbeing executed on the terminal, wherein the application for which theQoE estimation is to be performed is determined according to thepredicted probabilities, and wherein the information includes date,time, or day of the week.